WO2002044987A2 - System for determining a useful life of core deposits and interest rate sensitivity thereof - Google Patents
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- WO2002044987A2 WO2002044987A2 PCT/US2001/045101 US0145101W WO0244987A2 WO 2002044987 A2 WO2002044987 A2 WO 2002044987A2 US 0145101 W US0145101 W US 0145101W WO 0244987 A2 WO0244987 A2 WO 0244987A2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Asset management; Financial planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
Definitions
- the invention generally relates to financial forecasting and planning. More particularly, it concerns estimating the expected life of deposits, especially core deposits (a/k/a non-maturity deposits), at a financial institution, to ascertain the sensitivity of deposits to changes in variables determined outside the financial institution such as interest rate changes, and to forecast the behavior of deposits. 2. DESCRIPTION OF THE RELATED ART
- the profitability of a financial institution depends on its ability to earn higher interest rates on its assets than it pays for its deposits.
- core deposits a k/a non-maturity deposits
- categories such as NOW (Negotiable Order of Withdrawal), savings, checking and MMDA (money market demand accounts), are eligible to be withdrawn from the institution actually or virtually upon demand. If such deposits are used to buy longer maturity assets, a potentially serious asset and liability maturity mis-match is apparently created. In fact, however, a substantial fraction of core deposits tend to stay in an institution for a period measured in years rather than in days or weeks.
- OTS has been the most secretive about its internal process which is essentially a black box to those outside the OTS, although notes released and Fed publications indicate that OTS and Fed procedures are very similar.
- the Fed's published work indicates that it has examined a cross-section of financial institutions and has estimated the average lives of all types of core deposits to be very short, typically less than five years, with checking accounts in particular having a life of approximately one year.
- the Commerce Methodology also has the deficiency as with prior methodologies that it restricts the relationships between variables influencing retention rates to be linear. There is no reason, however, why Treasury rate changes, for example, would necessarily influence a financial institution's deposits in a strict linear fashion. Finally, the Commerce Methodology does not allow asymmetries in relationships. That is, an increase in deposit rates and a decrease in deposit rates are treated as though they have the same impact (with the sign reversed) on the level of deposits. That procedure does not accommodate a case such as when a depositor may choose to keep deposits in an institution with a rate increase but may choose to leave with a rate decrease, for example.
- Another object of the invention is to provide a system to determine the interest rate sensitivity of core deposits. Another object of the invention is to allow a financial institution to predict how interest rate changes will influence core deposits' expected lifetime and this value to the institution.
- Another object of the invention is to precisely and accurately forecast the retention rates of core deposits, the financial institution's interest rates, and the financial institution's total deposit balances all in the context of a single unified model.
- Another object is to provide a method of forecasting the expected life of core deposits, thereby to allow the financial institution to obtain the correct - and in many cases likely the longest possible - assets to match with the deposit base.
- Another object is as factors such as interest rates change, to forecast how deposits will change, thereby to permit the financial institution to determine how much risk to accept by stretching - or not stretching - the lives of assets.
- a method and system for determining a useful life of financial assets In a computerized system, a dynamic calculation of a first retention rate is performed for each of several financial assets. In the computerized system, a steady-state calculation of a second retention rate is performed for the financial assets. The first and second retention rates are combined to determine a predicted useful life of the combined financial assets.
- one of several variables affecting at least one of the retention rates is selected.
- a sensitivity of financial asset variables to other financial asset variables is determined. Scenarios are forecast, extrapolated from the retention rate.
- the financial assets include deposits and/or financial instruments.
- Data for each of the financial assets includes total deposit balances, deposit rates, and a sample of account balances. Data may be received for each of a the several financial assets.
- a length of the sample may be about four years.
- Outliers in the financial assets may be checked, in one variation of the invention.
- exogenous variables may be included in at least one of the calculations.
- the exogenous variables are selected from the set including seasonal variables, day-of-the-month variables, treasury interest rates, deposit rates, local unemployment rate, local personal income, and local retail sales, and the like.
- interest rate spread may be included in at least one of the calculations.
- forecast scenarios may include future values for use in at least one of the calculations.
- the future values may be selected from the set including forecast treasure rates, forecast horizon, forecast deposits, forecast retention rates, and forecast interest rates, and the like.
- the predicted useful life of the combined financial assets may be output, such as on a display.
- Fig. 1 is a block diagram illustrating data assembly.
- Fig. 2 is a flow chart illustrating basic estimation.
- Fig. 3 is a flow chart illustrating estimation alternatives.
- Fig. 4 is a flow chart illustrating forecasting procedure.
- Fig. 5 is a production schematic of MPS core deposits and CD behavior reports.
- the invention will be described herein in five parts for ease of understanding, without limitation.
- the first consists of a process for capturing or receiving initial data from a financial institution and putting the initial data in a predetermined format for use in subsequent processes.
- the second involves a process for selection of variables that influence retention rates in particular as well as total deposit balances and the institution's interest rates.
- the third involves alternatives allowed to the basic process described in the second part. That is, there is a basic approach to the estimation process but there are a number of alternatives that a financial institution can choose depending on the use of the forecasts.
- the fourth part is the actual forecasting process itself which is based on the estimation described in steps two and three. The forecasting process also allows an institution a number of alternatives depending on the use of the forecasts.
- the fifth step involves taking the results from the forecasting equation and using them in particular business applications, in particular, calculating the value of core deposits and their sensitivity to factors such as interest rate changes.
- the rest of this section describes each of these parts in more detail. Part One Reference is made to Fig. 1.
- Data is obtained from a financial institution, typically on three types of variables: (1) the institution's total deposit balances by type of deposit for a varying number of categories of deposits 107, (2) the institution's deposit rates 109, also by type of deposit for a varying number of categories of rates, and (3) a sample of the institution's individual accounts 103, again by type of deposit for a varying number of categories of accounts.
- the data is on a monthly basis and will be referred to hereafter as monthly although the general process can be employed on different frequency data, including quarterly, weekly or bi-weekly.
- the data on the institution's total balances and deposit rates typically do not need - should not need - further adjustment unless different deposit categories are being combined. (That is, the aggregate values are not modified unless the institution needs to combine multiple categories of deposits, for example, regular MMDA deposits and higher-yield MMDA deposits.)
- the sample of individual accounts however, often requires additional detail presented below.
- a survey sample size is determined 101.
- the process preferably employs a minimum of four years of monthly data for a minimum of 48 observations. This sample length provides enough information typically to obtain reasonable estimates and forecasts, although longer data samples are desired when available. (The maximum useful length of the data sample generally would be no more than ten years. Longer samples would be problematic due to issues of potentially changing market structures due to financial deregulation and innovation, for example.)
- Determining the size of the sample requires substantially more attention. How many depositor accounts should be included in the sample? The answer in part depends on how accurate the institution wants the forecast of the retention rate to be. The greater the desired accuracy, the larger the required sample of accounts. The answer also depends on the total pool of open accounts. The greater the number of accounts to sample from, the larger the required sample as well, although the relation between number of accounts and required sample size is highly nonlinear.
- n 4kV/d 2
- n the sample size
- k is based on the level of significance considered (typically 95% thus yielding a value of k of 1.96)
- s the standard deviation of the underlying population of deposit accounts
- d is the desired level of accuracy.
- the process here assumes experience based values for s that vary depending on the type of deposit. Assigning values employs information about the variable ultimately to be forecasted, the retention rate. Considering the retention rate as a probability of retention, the process then is a binomial event in statistics. We then pick a conservative (i.e. low) estimate of the likely retention rate for the type of deposit. For example, for checking accounts, a yearly retention rate of 70 percent should be considered conservative given prior applications. This value implies the appropriate value of s, which is used in the sample size equation.
- the second feature is to pick a particular value of d.
- This component does not have a single statistical requirement.
- the process lets the financial institution set this value although we normally recommend a value for d in the range of 0.02 to 0.03. Lower values imply more accuracy and thus require a larger sample and higher costs. Thus, the choice of the value of d is dependent on the institution's trade-off of accuracy versus cost.
- the third feature of the sample size determination is based on the recognition that data collection costs must be weighed against the benefits of improved accuracy.
- the above formula for n will yield a sample size in all cases.
- the last step in determining the sample size is simply to check the costs associated with collecting that size sample with the benefits of the accuracy that such a sample size would imply. From a practical perspective, this feature generally caps hand-collected sample sizes at 250-350 since larger samples generally do not yield appreciable improvements in forecast accuracy relative to increased collection costs.
- the process for checking for outliers advantageously is two part. Funds deposited in a non-maturity account may remain only overnight or for many years. When an account has a substantial temporary increase (or decrease) in deposits, however, one can reasonably question whether such a change is accurate or whether it has been mis- recorded. For example, in the transcription of account balances, it is possible to have an account with actual balances of, say, $3,456 mis-recorded as $34,566. Alternately, it is possible that the same type of change could be due to a large transitory deposit, for example as a result of selling a piece of property.
- This account would typically be deleted from the sample as unrepresentative of the accounts overall in the financial institution. Accounts with more than 5 percent of sample balances are "flagged" and examined, while accounts of that certain percent or more of sample balances typically are excluded. Ten percent is an appropriate percentage in the foregoing basic procedure, although other percentages will work. This last adjustment is utilized so that the focus of the process is not on one or a few individual accounts in the survey but rather on the total balances of all accounts in the survey. If one account or a few accounts, in fact, were to dominate behavior for a deposit class at a financial institution, then it would be inappropriate to delete the offending account. Note that in all cases, accounts are deleted only with the understanding and acceptance of the institution.
- the survey balances are aggregated for each account type and for each observation period to obtain the total survey balances by account type by period, referred to hereafter as survey balances 111.
- the data available by deposit type and by month consists of the institution's total balances 107, deposit rates 109, and survey balances 111.
- these variables are preferably in a spreadsheet 115 (e.g., Excel or Lotus) and arranged, for example, with the columns indicating the variables and the rows indicating the appropriate months.
- a spreadsheet 115 e.g., Excel or Lotus
- the first are simply seasonal and day-of-the-month type variables, so-called binary or "dummy" variables (0/1 values). For example, each month (e.g. January) has a separate identifying variable and each day of the week (e.g. for months ending on a Friday) also has a separate identifier. For example, a variable for Friday would take a value of 0 for all months that do not end on a Friday and would take a value of 1 for all months that end on Friday.
- the second external type of variable is Treasury interest rates. Three Treasury rates are included, for 90 day bills, for 1 year notes and for 10 year bonds. These three adequately capture most features of the term structure and the overall movement in general interest rate conditions.
- the third type of external variable is market deposit rates, that is, deposit rates for the typical institution in this financial institution's relevant market.
- Data may be gathered from a commercially available publication such as Bank Rate Monitor (BRM) or may be provided by the institution. These data need to be available for the same period over which the institution's survey balances are available.
- BRM Bank Rate Monitor
- additional local market condition descriptors may be entered as external variables. These are included to measure the health of the local or regional economy. These differ in availability across markets but in general include the unemployment rate, personal income and retail sales.
- the spreadsheet process 115 simply puts the data in the appropriate format to begin the process of the statistical analysis that leads to the forecasts. The spreadsheet is saved and the spreadsheet then serves as an input, advantageously being incorporated directly into a statistical package such as RATS (Regression Analysis of Time Series). Part Two
- Fig. 2 This part describes exemplary basic statistical procedures employed to obtain equations that are used to forecast variables for the financial institution.
- the first step in this part is to define some additional variables 203.
- a financial institution's deposits may be influenced either by the general level of interest rates or by interest rate spreads.
- the relevant interest rate spreads potentially are of three types: differences between this institution's deposit rates and those in the market, differences between this institution's rates on different types of deposits, and differences between short-term and long-term rates. All three types of spread variables are defined and are potentially included in subsequent analysis although the exact definitions of the spreads are necessarily institution specific. One or more of three types of spreads are included: (1) differences between short-term and long-term rates, e.g. between the 90 day and 1 year Treasury rates and between the 1 year and 10 year Treasury rates, (2) differences between this institution's rate on a particular deposit category and that in its market, e.g. differences between this institution's MMDA rate and the market (e.g., BRM) MMDA rate, and (3) differences between this institution's rates are different deposit categories, e.g., MMDA and NOW rates.
- differences between short-term and long-term rates e.g. between the 90 day and 1 year Treasury rates and between the 1 year and 10 year Treasury rates
- the process allows the potential for institution-specific variables that cannot be picked up by economic variables as typically defined. For example, if an institution had a promotion on MMDA accounts from May to July giving free checking if a minimum balance were maintained in an MMDA account, one might suspect that MMDA balances would increase in those months independent of changes in other variables such as interest rates. Thus it would be appropriate to define and include a specific binary variable that assumed a value of 1 during the months of May through July and 0 otherwise. The definition of institution- specific binary variables is determined based on information provided by the financial institution and in consultation with management.
- the OLS procedure involves relating a variable at a point in time t, defined as Y t , e.g. checking account balances, to prior values of this variable, denoted Y t . L , as well as to prior values - and thus known values - of variables in the other three blocks. These could be denoted as Y2 t . L .
- Y t also is potentially related to current and to lagged values of the exogenous variables, labeled X t.L .
- Y t ⁇ 0 + ⁇ , Y t . L + ⁇ 2 Y2 t . L + ⁇ 3 X t . L
- X exogenous variable
- Y variable
- t time
- L lag time
- VAR vector autoregressive
- VAR analysis The general estimation process 217 adopted here is called VAR analysis.
- the preferred approach is called subset VAR analysis and has been previously employed in
- the first part of reducing the dimensionality of the VAR is to determine the criteria whereby variables are to be included or excluded: lag selection criteria 219.
- a number of statistical criteria are available as described in Hafer and Sheehan, op. cit.
- the basic criterion preferably employed here is the Bayesian Information Criterion (BIC) which has the property that the selection of variables included is the theoretically correct choice at least asymptotically. (The process allows other criteria to be chosen and that will be discussed below.)
- BIC Bayesian Information Criterion
- the additional assumptions employed in the VAR order of estimation process 215 are straightforward. There are a total of four blocks of variables as noted above 205, three within the institution and one exogenous. The process constrains relations between the blocks.
- the blocks can be labeled T for the vector of variables included in total balances, S for the vector of survey balance variables, R for the vector of deposit rates, and X for the vector of exogenous variables.
- the vector X is assumed to be determined independent of any considerations in the other three vectors. That is, the financial institution does not have any impact on market rates such as Treasury bill rates.
- the vector S for survey balances is assumed to be potentially influenced by deposit rates but not by total balances.
- the vector T is assumed to be potentially influenced both by deposit rates and by survey balances (and thus retention rates).
- the vector R potentially influences both survey and total balances and is potentially influenced by those in turn. That is, the institution may change deposit rates either in response to changing retention rates or in response to changes in total balances.
- the statistical properties of the T, S and R vectors require that changes in T and S influence the levels of R since R typically is an 1(0) series while T and S typically are 1(1) series. See James D. Hamilton, Time Series Analysis (Princeton, N.J., Princeton University Press, 1994) for a complete discussion of the meaning and differences between 1(0) and 1(1) series).
- Part Two the process yields the basic estimation equations or estimate regressions 221 based on a subset VAR model that are employed to forecast retention rates in particular but also potentially deposit rates and total balances.
- Part Three goes through alternatives in much more detail, in particular, alternatives that further distinguish this approach from any others employed previously.
- deposits and loans can be examined in an analogous manner 315.
- This utilizes two modifications to the above discussion.
- One is the explicit consideration of three additional blocks, one for total assets (A), one for survey assets (B), and one for the interest rates on those assets (C); the other is to consider the implications of levels versus the changes in those assets.
- total assets are allowed to influence asset rates but not survey assets; survey assets may influence both asset rates and total assets; and asset rates may influence both total assets and survey assets.
- asset rates may influence deposit rates and vice-versa while survey assets do not influence survey deposits (or the reverse) at least directly and generally total assets do not influence total deposits (or the reverse) again at least directly.
- the first set of restrictions are identical to those for deposits. The only additional consideration when dealing with assets rather than deposits concerns a technical issue of examining levels versus rates of change.
- the analysis considers the level of deposits as a prelude to calculating the retention rate of survey deposit balances rather than focusing on the rate of change.
- survey assets for example, the outstanding survey balances of VISA cards, there is a question of whether it is more appropriate to consider the level of VISA balances or the change in the amount of those balances, that is, the amount paid off each month.
- the process focuses on the level rather than the rate of change.
- the forecasting procedure is robust enough to generate forecasts of balance payments if that was of interest to the financial institution.
- This modification also allows a breakdown of deposit categories into generally separate blocks, e.g., personal vs. business, where personal would refer to the vectors T, S and R mentioned earlier and business would refer to the vectors A, B and C.
- the three basic "building blocks" can be repeated and joined together as necessary, multiple times as necessary. Part Four Reference is made to Fig. 4.
- the forecasting itself is based on the simultaneously estimated system of equations 317 from a subset VAR and their repeated use.
- Y first endogenous variable
- X second endogenous variable
- t time.
- the first assumption concerns the treatment of the so-called external (exogenous) variables. That is, some variables such as market interest rates and Treasury interest rates are taken as given by the financial institution.
- external (exogenous) variables such as market interest rates and Treasury interest rates are taken as given by the financial institution.
- future values or forecasts
- Generating those forecasts is two part.
- Treasury rates are forecast first. The standard initial "forecast" is to simply assume they will remain unchanged. However, the process allows the institution to define specific hypothetical changes or to forecast Treasury rates also using a VAR model.
- the second assumption concerns the forecast horizon.
- the appropriate question is what is the application of the forecast?
- the answer to that question normally determines the appropriate horizon.
- the process allows the institution to specify two different horizons. One is a monthly horizon; for how many individual months in the future does the institution want monthly forecasts? The other is a yearly forecast; that is, how far into the future does the institution want end-of-year value forecasts?
- the procedure is general and allows virtually any forecast horizon.
- the standard horizons chosen tend to be one or two years of monthly forecasts and 15 to 20 years of yearly forecasts.
- the process allows three different approaches to the generation of those additional forecasts based on different patterns of Treasury rate behaviors 407.
- the first is what is referred to as the standard regulatory approach. That is, Treasury rates are increased or decreased by a fixed amount, e.g. 100, 200 or 300 basis points, and then the estimated equations are employed using as inputs those higher or lower Treasury rates.
- the second choice allowed is to determine what variables - or what blocks of variables - will be allowed to vary in the forecast 409.
- the base case allows all variables to vary freely depending on the specified equations.
- the first alternative is called "balances constant 413.” In this case, all total balances are constrained to remain constant at their last observed value.
- the focus in this case typically is on the deposit rates with the question of how do deposit rates have to change with a given change in Treasury (and thus market) rates in order to maintain deposit balances.
- the other alternative is analogous and is called “rates constant 411."
- deposit rates are constrained to remain constant at their last observed value. In this scenario the focus typically is on what would happen to deposit balances if the financial institution did not alter rates, especially in the face of changing market rates.
- the third choice allowed considers the type of forecast generated, dynamic or steady-state 417.
- the base case considers the dynamic forecast and simply uses the most recent values, i.e. those available at time 0, to forecast the next period values, say at time 1 , which are in turn used to forecast values in the subsequent period.
- the advantage to this approach is that it yields real-time forecasts based on the actual values and the history of the variables being forecast.
- the disadvantage is that in some cases it may be sensitive to outliers or unusual observations in the last period or two of the sample. That is, if in the last observation period there was a dramatic decrease in checking account balances, for example, the forecasts based on that may view that decrease as a permanent change and may extrapolate that into continuing decreases in the future.
- Forecasts of retained balances behaviors by time period are also conducted for hypothetical situations where interest rates rise or decline in the future (multiple choices of specific future interest rate paths can be accommodated).
- Indicated changes in retained balance behaviors across interest rate scenarios provide managers with specific measurements of the rate sensitivity of retention behavior. This information is the source for quantifying category average lives, present values, premiums, and associated inputs for use, e.g., in ALM models.
- the indicated changes in category average lives, etc., as interest rates vary i.e., their sensitivity to rate changes) provides a quantified measure of the convexity of the category's value.
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US09/726,346 US7328179B2 (en) | 2000-12-01 | 2000-12-01 | System for determining a useful life of core deposits and interest rate sensitivity thereof |
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US9374370B1 (en) | 2015-01-23 | 2016-06-21 | Island Intellectual Property, Llc | Invariant biohash security system and method |
CN112037030A (en) * | 2020-09-01 | 2020-12-04 | 中国银行股份有限公司 | Bank large-amount deposit interest rate query method and device |
US20220309525A1 (en) * | 2021-03-29 | 2022-09-29 | Mckinsey & Company, Inc. | Machine learning model for predicting client sensitivity to rate changes in commercial deposit products |
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US5812988A (en) * | 1993-12-06 | 1998-09-22 | Investments Analytic, Inc. | Method and system for jointly estimating cash flows, simulated returns, risk measures and present values for a plurality of assets |
US6052673A (en) * | 1985-08-27 | 2000-04-18 | Trans Texas Holdings Corporation | Investment management |
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US5644727A (en) * | 1987-04-15 | 1997-07-01 | Proprietary Financial Products, Inc. | System for the operation and management of one or more financial accounts through the use of a digital communication and computation system for exchange, investment and borrowing |
WO1995006290A2 (en) * | 1993-08-18 | 1995-03-02 | Wells Fargo Nikko Investment Advisors | Investment fund management method and system |
AU4440200A (en) * | 1999-05-04 | 2000-11-17 | Anthony Alfred Street | Capital project appraisal system |
US6363360B1 (en) * | 1999-09-27 | 2002-03-26 | Martin P. Madden | System and method for analyzing and originating a contractual option arrangement for a bank deposits liabilities base |
US7328179B2 (en) * | 2000-12-01 | 2008-02-05 | Mcguire Performance Solutions, Inc. | System for determining a useful life of core deposits and interest rate sensitivity thereof |
-
2000
- 2000-12-01 US US09/726,346 patent/US7328179B2/en active Active
-
2001
- 2001-12-03 WO PCT/US2001/045101 patent/WO2002044987A2/en not_active Application Discontinuation
- 2001-12-03 AU AU2002219994A patent/AU2002219994A1/en not_active Abandoned
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
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US6052673A (en) * | 1985-08-27 | 2000-04-18 | Trans Texas Holdings Corporation | Investment management |
US5812988A (en) * | 1993-12-06 | 1998-09-22 | Investments Analytic, Inc. | Method and system for jointly estimating cash flows, simulated returns, risk measures and present values for a plurality of assets |
Also Published As
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AU2002219994A1 (en) | 2002-06-11 |
US20020069147A1 (en) | 2002-06-06 |
WO2002044987A3 (en) | 2002-08-08 |
US7328179B2 (en) | 2008-02-05 |
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